GPMeta: a GPU-accelerated method for ultrarapid pathogen identification from metagenomic sequences.

Metagenomic sequencing (mNGS) is a powerful diagnostic tool to detect causative pathogens in clinical microbiological testing owing to its unbiasedness and substantially reduced costs. Rapid and accurate classification of metagenomic sequences is a critical procedure for pathogen identification in dry-lab step of mNGS test. However, clinical practices of the testing technology are hampered by the challenge of classifying sequences within a clinically relevant timeframe. Here, we present GPMeta, a novel GPU-accelerated approach to ultrarapid pathogen identification from complex mNGS data, allowing users to bypass this limitation. Using mock microbial community datasets and public real metagenomic sequencing datasets from clinical samples, we show that GPMeta has not only higher accuracy but also significantly higher speed than existing state-of-the-art tools such as Bowtie2, Bwa, Kraken2 and Centrifuge. Furthermore, GPMeta offers GPMetaC clustering algorithm, a statistical model for clustering and rescoring ambiguous alignments to improve the discrimination of highly homologous sequences from microbial genomes with average nucleotide identity >95%. GPMetaC exhibits higher precision and recall rate than others. GPMeta underlines its key role in the development of the mNGS test in infectious diseases that require rapid turnaround times. Further study will discern how to best and easily integrate GPMeta into routine clinical practices. GPMeta is freely accessible to non-commercial users at https://github.com/Bgi-LUSH/GPMeta.

Whole-genome and Transcriptome Sequencing of Prostate Cancer Identify New Genetic Alterations Driving Disease Progression.

BACKGROUND: Global disparities in prostate cancer (PCa) incidence highlight the urgent need to identify genomic abnormalities in prostate tumors in different ethnic populations including Asian men. OBJECTIVE: To systematically explore the genomic complexity and define disease-driven genetic alterations in PCa. DESIGN, SETTING, AND PARTICIPANTS: The study sequenced whole-genome and transcriptome of tumor-benign paired tissues from 65 treatment-naive Chinese PCa patients. Subsequent targeted deep sequencing of 293 PCa-relevant genes was performed in another cohort of 145 prostate tumors. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The genomic alteration landscape in PCa was analyzed using an integrated computational pipeline. Relationships with PCa progression and survival were analyzed using nonparametric test, log-rank, and multivariable Cox regression analyses. RESULTS AND LIMITATIONS: We demonstrated an association of high frequency of CHD1 deletion with a low rate of TMPRSS2-ERG fusion and relatively high percentage of mutations in androgen receptor upstream activator genes in Chinese patients. We identified five putative clustered deleted tumor suppressor genes and provided experimental and clinical evidence that PCDH9, deleted/loss in approximately 23% of tumors, functions as a novel tumor suppressor gene with prognostic potential in PCa. Furthermore, axon guidance pathway genes were frequently deregulated, including gain/amplification of PLXNA1 gene in approximately 17% of tumors. Functional and clinical data analyses showed that increased expression of PLXNA1 promoted prostate tumor growth and independently predicted prostate tumor biochemical recurrence, metastasis, and poor survival in multi-institutional cohorts of patients with PCa. A limitation of this study is that other genetic alterations were not experimentally investigated. CONCLUSIONS: There are shared and salient genetic characteristics of PCa in Chinese and Caucasian men. Novel genetic alterations in PCDH9 and PLXNA1 were associated with disease progression. PATIENT SUMMARY: We reported the first large-scale and comprehensive genomic data of prostate cancer from Asian population. Identification of these genetic alterations may help advance prostate cancer diagnosis, prognosis, and treatment.

SOAPfusion: a robust and effective computational fusion discovery tool for RNA-seq reads.

MOTIVATION: RNA-Seq provides a powerful approach to carry out ab initio investigation of fusion transcripts representing critical translocation and post-transcriptional events that recode hereditary information. Most of the existing computational fusion detection tools are challenged by the issues of accuracy and how to handle multiple mappings. RESULTS: We present a novel tool SOAPfusion for fusion discovery with paired-end RNA-Seq reads. SOAPfusion is accurate and efficient for fusion discovery with high sensitivity (≥93%), low false-positive rate (≤1.36%), even the coverage is as low as 10×, highlighting its ability to detect fusions efficiently at low sequencing cost. From real data of Universal Human Reference RNA (UHRR) samples, SOAPfusion detected 7 novel fusion genes, more than other existing tools and all genes have been validated through reverse transcription-polymerase chain reaction followed by Sanger sequencing. SOAPfusion thus proves to be an effective method with precise applicability in search of fusion transcripts, which is advantageous to accelerate pathological and therapeutic cancer studies.

SOAPsplice: Genome-Wide ab initio Detection of Splice Junctions from RNA-Seq Data.

RNA-Seq, a method using next generation sequencing technologies to sequence the transcriptome, facilitates genome-wide analysis of splice junction sites. In this paper, we introduce SOAPsplice, a robust tool to detect splice junctions using RNA-Seq data without using any information of known splice junctions. SOAPsplice uses a novel two-step approach consisting of first identifying as many reasonable splice junction candidates as possible, and then, filtering the false positives with two effective filtering strategies. In both simulated and real datasets, SOAPsplice is able to detect many reliable splice junctions with low false positive rate. The improvement gained by SOAPsplice, when compared to other existing tools, becomes more obvious when the depth of sequencing is low. SOAPsplice is freely available at http://soap.genomics.org.cn/soapsplice.html.

Deep RNA sequencing at single base-pair resolution reveals high complexity of the rice transcriptome.

Understanding the dynamics of eukaryotic transcriptome is essential for studying the complexity of transcriptional regulation and its impact on phenotype. However, comprehensive studies of transcriptomes at single base resolution are rare, even for modern organisms, and lacking for rice. Here, we present the first transcriptome atlas for eight organs of cultivated rice. Using high-throughput paired-end RNA-seq, we unambiguously detected transcripts expressing at an extremely low level, as well as a substantial number of novel transcripts, exons, and untranslated regions. An analysis of alternative splicing in the rice transcriptome revealed that alternative cis-splicing occurred in approximately 33% of all rice genes. This is far more than previously reported. In addition, we also identified 234 putative chimeric transcripts that seem to be produced by trans-splicing, indicating that transcript fusion events are more common than expected. In-depth analysis revealed a multitude of fusion transcripts that might be by-products of alternative splicing. Validation and chimeric transcript structural analysis provided evidence that some of these transcripts are likely to be functional in the cell. Taken together, our data provide extensive evidence that transcriptional regulation in rice is vastly more complex than previously believed.